# Load data
data <- as.data.frame(t(readRDS(paste0(data_path, "ROSMAP_RINPMIAGESEX_resids.rds"))))
covs <- readRDS(paste0(data_path, "ROSMAP_RINPMIAGESEX_covs.rds"))
stopifnot(identical(covs$mrna_id, colnames(data)))
covs <- covs[, c("ceradsc", "braaksc", "cogdx", "neuroStatus")]
covs$ceradsc <- as.factor(covs$ceradsc)
covs$braaksc <- as.factor(covs$braaksc)
covs$cogdx <- as.factor(covs$cogdx)
covs$neuroStatus <- as.factor(covs$neuroStatus)##
## 0 1
## 168 174
r <- testAllCutoffs(exprData=input,
target=toPredict,
covs=covs,
train.split=0.7,
nfolds=5,
t=10,
path=paste0(tgcn_path),
targetName="APP",
tissueName="ROSMAP",
seed=1234,
cutoffs=10:1,
n=100,
m=10,
s=10,
minCor=0.3,
maxTol=3,
save=T,
overwrite=F)p <- lapply(r$nets, function(cutoff) cutoff$GOenrich$plotStats)
ggarrange(p$c8 + theme(text=element_text(size=10)) + scale_y_continuous(limits=c(0,100)),
p$c9 + theme(text=element_text(size=10)) + scale_y_continuous(limits=c(0,100)),
p$c10 + theme(text=element_text(size=10)) + scale_y_continuous(limits=c(0,100)),
ncol=3, nrow=1, common.legend=T, legend="bottom")` ### Reduced GO terms at TGCN level
go_results <- r$nets$c8$GOenrich$terms
plots <- getReducedTermsPlots(go_results, module=T)
saveRDS(plots, paste0(tgcn_path, "plots_APP.rds"))##
## RPS10 HNRNPA2B1 ATP1B1 PPP2CA RPL36 TPPP FBXO11 SCN8A
## 215 85 79 66 63 55 53 53
## NFASC NCL GNAI1 ABCD3 PTAR1 YWHAE DEAF1 SERINC3
## 52 44 22 16 16 14 12 11
## FRZB FBXO9 CEP250 DGCR6L SCAF4 hubs PRKAR1A ZFPM1
## 11 6 5 5 3 3 1 1
## ASTE1 PLPP5
## 1 1